Tarek Elouaret, Stéphane Zuckerman, L. Kessal, Yoan Espada, N. Cuperlier, Guillaume Bresson, F. Ouezdou, Olivier Romain
{"title":"Position Paper: Prototyping Autonomous Vehicles Applications with Heterogeneous Multi-FpgaSystems","authors":"Tarek Elouaret, Stéphane Zuckerman, L. Kessal, Yoan Espada, N. Cuperlier, Guillaume Bresson, F. Ouezdou, Olivier Romain","doi":"10.1109/UCET.2019.8881834","DOIUrl":null,"url":null,"abstract":"One important feature required by autonomous vehicles is the ability to perform a localization task in order to navigate in both known (urban, suburban, and highways) and unknown environments. Instead of relying on LIDAR technology, we propose to leverage a bio-inspired algorithm relying on more conventional cameras and a large neural network (NN) [2]. Yet, this approach must be able to scale. We propose to investigate the development of an FPGA-based solution. Due to the size of NN, dynamic partial reconfiguration will be required, and an efficient (software-based) scheduler must place the hardware tasks on multiple FPGA chip. We intend to implement this algorithm using a unique custom board, Wizarde, which embeds a 3 × 3 matrix of Zynq SoCs with high-end FPGAs to prototype a possible solution.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 UK/ China Emerging Technologies (UCET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UCET.2019.8881834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
One important feature required by autonomous vehicles is the ability to perform a localization task in order to navigate in both known (urban, suburban, and highways) and unknown environments. Instead of relying on LIDAR technology, we propose to leverage a bio-inspired algorithm relying on more conventional cameras and a large neural network (NN) [2]. Yet, this approach must be able to scale. We propose to investigate the development of an FPGA-based solution. Due to the size of NN, dynamic partial reconfiguration will be required, and an efficient (software-based) scheduler must place the hardware tasks on multiple FPGA chip. We intend to implement this algorithm using a unique custom board, Wizarde, which embeds a 3 × 3 matrix of Zynq SoCs with high-end FPGAs to prototype a possible solution.